Turing Test, Chinese Room Argument, Symbol Grounding Problem. Meanings in Artificial Agents.
Turing Test (TT), Chinese Room Argument (CRA) & Symbol Grounding Problem (SGP) are basic tools for investigating the possibilities for artificial agents (AAs) to behave as humans.
An analysis of these three tools has been proposed under the banner of “meaning generation” (AISB/IACAP 2012 presentation). It is shown that the capability for an AA to generate human like meanings can be a reading of TT, CRA & SGP (1). We use the Meaning Generator System (MGS) that can be used for any agent submitted to a constraint (2). The concerns are that animal and human constraints are not today transferrable to AAs. An entry point is proposed by extending a “stay alive” constraint to AAs.
Ethical concerns are highlighted. Continuations are proposed.
Paper is available at http://cogprints.org/8716/ and in proceedings at http://www.mrtc.mdh.se/~gdc/work/AISB-IACAP-2012/NaturalComputingProceedings-2012-06-22.pdf
Christophe Menant
(1) http://crmenant.free.fr/IACAP-AISB2012-C.Menant-050712.pdf
(2) http://cogprints.org/3694/

IACAP 2011 presentation on cognition as management of meanings:
Cognition is proposed as a management of meanings for agents that have constraints to satisfy (stay alive, look for happiness, avoid obstacles, …).
(IACAP 2011 presentation http://cogprints.org/7584/)
The systemic approach to meaning generation for agents is used in an evolutionary perspective to cognition where robots are agents with derived constraints, meanings and intentionality.
Christophe Menant (http://crmenant.free.fr/Home-Page/index.HTM)

Systemic approach to meaningful
representations.
An existing systemic
approach
to meaning generation (usable for animals, humans
and robots) has recently
been extended to the notion of meaningful
representation.
The starting point is a system submitted to a
constraint (stay alive, avoid
obstacle, ..) that receives information from its
environment and compares
it with its constraint.
The generated meaning is the connection existing
between the received information
and the constraint. It triggers an action aimed at
satisfying the constraint
(avoid predator, turn right/left, ..).
A Meaning Generator System (MGS) has been introduced.
It can be used for any
agent submitted to constraints.
The notion of meaningful representation of an entity
for an agent is defined
as being the networks of meanings related to the
entity, with the action scenarios.
See
book chapter and IACAP
2011 proposed abstract
Such meaningful representations are used in the
approach on artificial consciousness
based on self-consciousness (http://robots.ne
t/article/2244.html).
More is to come on this. Christophe Menant